Date 
Lecture 
Notes etc 
Wed 9/8 
Lecture 1: introduction
pdf slides,
6 per page


Mon 9/13 
Lecture 2: linear regression, estimation, generalization
pdf slides,
6 per page

(Jordan: ch 66.3) 
Wed 9/15 
Lecture 3: additive regression, overfitting, crossvalidation, statistical view
pdf slides,
6 per page


Mon 9/20 
Lecture 4: statistical regression, uncertainty,
active learning
pdf slides,
6 per page


Wed 9/22 
Lecture 5: from
regression to classification, decision theory, logistic regression
pdf slides,
6 per page


Mon 9/27 
Lecture 6: logistic regression, regularization,
discriminative classification
pdf slides,
6 per page


Wed 9/29 
Lecture 7: support vector machines, kernels
pdf slides,
6 per page

Notes on Lagrange multipliers (postscript)
Optional reading:
Burges (postscript)

Mon 10/4 
Lecture 8: kernel methods, kernels
pdf slides,
6 per page


Wed 10/6 
Lecture 9: feature selection, combination
of methods, forwardfitting
pdf slides,
6 per page


Wed 10/13 
MIDTERM: in class


Mon 10/18 
Lecture 10: boosting
pdf slides,
6 per page

Optional reading:
Schapire et al (postscript)
Friedman et al (postscript)

Wed 10/20 
Lecture 11: complexity, VCdimension,
learning
pdf slides,
6 per page


Mon 10/25 
Lecture 12: VCbounds, structural
risk minimization, compression and model selection
pdf slides,
6 per page


Wed 10/27 
Lecture 13: Minimum description length principle; structure, mixtures, and the EMalgorithm
pdf slides,
6 per page


Mon 11/1 
Lecture 14: The EMalgorithm and Gaussian mixtures; convergence, regularization, and classification
pdf slides,
6 per page


Wed 11/3 
Lecture 15: Mixture classifiers, mixtures of experts; nonparametric mixtures; clustering
pdf slides,
6 per page


Mon 11/8 
Lecture 16: clustering; kmeans and spectral.
pdf slides,
6 per page


Wed 11/10 
Lecture 17: clustering; semisupervised and model based
pdf slides,
6 per page


Mon 11/15 
Lecture 18: Hidden Markov Models
pdf slides,
6 per page


Wed 11/17 
Lecture 19: HMMs, EM, viterbi
pdf slides,
6 per page


Mon 11/22 
Lecture 20: Graphical models (Bayesian networks)
pdf slides,
6 per page


Wed 11/24 
Lecture 21: Undirected graphical models, medical diagnosis, inference and messages
pdf slides,
6 per page


Mon 11/29 
Lecture 22: Exact probabilistic inference,
message passing
pdf slides,
6 per page


Wed 12/1 
Lecture 23: Exact inference and junction trees; learning Bayesian networks
pdf slides,
6 per page

Projects due Fri Dec 3!

Mon 12/6 
Lecture 24: Learning Bayesian networks; review for the final
pdf slides,
6 per page


Wed 12/8 
FINAL EXAM: in class

